Statistical routines and probability distributions.
Chi-squared distribution standard deviation.
Beta distribution standard deviation.
Gamma distribution standard deviation.
Inverse gamma distribution standard deviation.
Calculate the standard deviation of a strided array using a two-pass algorithm.
Calculate the standard deviation of a strided array.
Seamless REST/GraphQL API mocking library for browser and Node.js.
Normal distribution standard deviation.
Oxc Parser Node API
Small javascript / nodejs module to calculate the standard deviation
Get the standard deviation in an array
return the standard deviation of an array or numeric argument list
Student's t distribution standard deviation.
🔎 A simple, tiny and lightweight benchmarking library!
A Node-RED node that provides several simple smoothing algorithms for incoming data values.
Lognormal distribution standard deviation.
Compute a corrected sample standard deviation incrementally.
An implementation of the WHATWG URL Standard's URL API and parsing machinery
Browser-friendly inheritance fully compatible with standard node.js inherits()
Normal distribution quantile function.
Poisson distribution standard deviation.
Arcsine distribution standard deviation.
Normal distribution probability density function (PDF).
Population and sample standard deviation helpers over f64 slices.
Composable statistical primitives for RustUse.
An implementation of the standard deviation calculation in C, with much better performance (50x-100x) than using pure ruby.
This class uses Kalman Filter updating to tally sample mean and sum of squared deviations from the average, along with min, max, and sample size. Sample variance, standard deviation, and standard error are calculated on demand.
A Ruby gem that extends all Enumerable objects (Arrays, Ranges, Sets, etc.) with essential statistical methods. Provides mean, median, variance, and standard deviation calculations, along with robust outlier detection using the IQR method. Perfect for data analysis, performance monitoring, A/B testing, and cleaning datasets with extreme values. Zero dependencies and works seamlessly with any Ruby collection that includes Enumerable.